Establishment and validation of risk prediction model to predict intravenous immunoglobulin-resistance in Kawasaki disease based on meta-analysis of 15 cohorts
Abstract Background Pediatric Kawasaki disease (KD) patients showing resistance to intravenous immunoglobulin (IVIG) are at risk of coronary artery lesions; thus, early prediction of IVIG resistance is particularly important. Herein, we aimed to develop and verify a novel predictive risk model for I...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
|
| Series: | Italian Journal of Pediatrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13052-025-01889-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849724125567778816 |
|---|---|
| author | Shuhui Wang Na Sun PanPan Liu Weiguo Qian Qiuqin Xu DaoPing Yang Mingyang Zhang Miao Hou Ye Chen Guanghui Qian Chunmei Gao Ling Sun Haitao Lv |
| author_facet | Shuhui Wang Na Sun PanPan Liu Weiguo Qian Qiuqin Xu DaoPing Yang Mingyang Zhang Miao Hou Ye Chen Guanghui Qian Chunmei Gao Ling Sun Haitao Lv |
| author_sort | Shuhui Wang |
| collection | DOAJ |
| description | Abstract Background Pediatric Kawasaki disease (KD) patients showing resistance to intravenous immunoglobulin (IVIG) are at risk of coronary artery lesions; thus, early prediction of IVIG resistance is particularly important. Herein, we aimed to develop and verify a novel predictive risk model for IVIG resistance in KD based on meta-analyses. Methods PubMed, Embase, and Web of Science databases were searched for cohort studies on the risk factors for IVIG resistance from January 2006 to December 2022. Data were extracted from the screened literature, followed by quality assessment using the Newcastle-Ottawa scale. meta-analyses used Stata 17.0 software to extract the risk factors with significant combined effect sizes and combined risk values, followed by logistic regression prediction model construction. The model was prospective validated using data from 1007 pediatric KD cases attending the Children’s Hospital of Soochow University. The model’s predictive ability was assessed using the Hosmer–Lemeshow test and area under the receiver operating characteristic curve (AUC) and the clinical utility was assessed using decision curve analysis(DCA). Results Fifteen cohort studies reporting 4273 patients with IVIG resistance were included. The incidence of IVIG resistance was 16.2%. Six risk factors were reported ≥ 3 times with significant results for the combined effect size: male sex, rash, cervical lymphadenopathy, % neutrophils ≥ 80%, Age ≤ 12 months and platelet count ≤ 300 × 109/L. The logistic scoring model had 83.8% specificity, 70.4% sensitivity, and an optimal cut-off value of 23.500. Conclusion The risk prediction model for IVIG resistance in KD showed a good predictive performance, and pediatricians should pay high attention to these high-risk patients and develop an appropriate individual regimens to prevent coronary complications. |
| format | Article |
| id | doaj-art-033c847976e1468fa1282a13cecdfa67 |
| institution | DOAJ |
| issn | 1824-7288 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Italian Journal of Pediatrics |
| spelling | doaj-art-033c847976e1468fa1282a13cecdfa672025-08-20T03:10:50ZengBMCItalian Journal of Pediatrics1824-72882025-02-0151111210.1186/s13052-025-01889-wEstablishment and validation of risk prediction model to predict intravenous immunoglobulin-resistance in Kawasaki disease based on meta-analysis of 15 cohortsShuhui Wang0Na Sun1PanPan Liu2Weiguo Qian3Qiuqin Xu4DaoPing Yang5Mingyang Zhang6Miao Hou7Ye Chen8Guanghui Qian9Chunmei Gao10Ling Sun11Haitao Lv12Department of Cardiology, Children’s Hospital of Soochow UniversityDepartment of Health Statistics, School of Public Health, Shandong Second Medical UniversityDepartment of Cardiology, Children’s Hospital of Soochow UniversityDepartment of Cardiology, Children’s Hospital of Soochow UniversityDepartment of Cardiology, Children’s Hospital of Soochow UniversityDepartment of Cardiology, Children’s Hospital of Soochow UniversityDepartment of Cardiology, Children’s Hospital of Soochow UniversityDepartment of Cardiology, Children’s Hospital of Soochow UniversityDepartment of Cardiology, Children’s Hospital of Soochow UniversityDepartment of Cardiology, Children’s Hospital of Soochow UniversityDepartment of Cardiology, Children’s Hospital of Soochow UniversityDepartment of Cardiology, Children’s Hospital of Soochow UniversityDepartment of Cardiology, Children’s Hospital of Soochow UniversityAbstract Background Pediatric Kawasaki disease (KD) patients showing resistance to intravenous immunoglobulin (IVIG) are at risk of coronary artery lesions; thus, early prediction of IVIG resistance is particularly important. Herein, we aimed to develop and verify a novel predictive risk model for IVIG resistance in KD based on meta-analyses. Methods PubMed, Embase, and Web of Science databases were searched for cohort studies on the risk factors for IVIG resistance from January 2006 to December 2022. Data were extracted from the screened literature, followed by quality assessment using the Newcastle-Ottawa scale. meta-analyses used Stata 17.0 software to extract the risk factors with significant combined effect sizes and combined risk values, followed by logistic regression prediction model construction. The model was prospective validated using data from 1007 pediatric KD cases attending the Children’s Hospital of Soochow University. The model’s predictive ability was assessed using the Hosmer–Lemeshow test and area under the receiver operating characteristic curve (AUC) and the clinical utility was assessed using decision curve analysis(DCA). Results Fifteen cohort studies reporting 4273 patients with IVIG resistance were included. The incidence of IVIG resistance was 16.2%. Six risk factors were reported ≥ 3 times with significant results for the combined effect size: male sex, rash, cervical lymphadenopathy, % neutrophils ≥ 80%, Age ≤ 12 months and platelet count ≤ 300 × 109/L. The logistic scoring model had 83.8% specificity, 70.4% sensitivity, and an optimal cut-off value of 23.500. Conclusion The risk prediction model for IVIG resistance in KD showed a good predictive performance, and pediatricians should pay high attention to these high-risk patients and develop an appropriate individual regimens to prevent coronary complications.https://doi.org/10.1186/s13052-025-01889-wKawasaki diseaseIntravenous immunoglobulinRisk factorPrediction model |
| spellingShingle | Shuhui Wang Na Sun PanPan Liu Weiguo Qian Qiuqin Xu DaoPing Yang Mingyang Zhang Miao Hou Ye Chen Guanghui Qian Chunmei Gao Ling Sun Haitao Lv Establishment and validation of risk prediction model to predict intravenous immunoglobulin-resistance in Kawasaki disease based on meta-analysis of 15 cohorts Italian Journal of Pediatrics Kawasaki disease Intravenous immunoglobulin Risk factor Prediction model |
| title | Establishment and validation of risk prediction model to predict intravenous immunoglobulin-resistance in Kawasaki disease based on meta-analysis of 15 cohorts |
| title_full | Establishment and validation of risk prediction model to predict intravenous immunoglobulin-resistance in Kawasaki disease based on meta-analysis of 15 cohorts |
| title_fullStr | Establishment and validation of risk prediction model to predict intravenous immunoglobulin-resistance in Kawasaki disease based on meta-analysis of 15 cohorts |
| title_full_unstemmed | Establishment and validation of risk prediction model to predict intravenous immunoglobulin-resistance in Kawasaki disease based on meta-analysis of 15 cohorts |
| title_short | Establishment and validation of risk prediction model to predict intravenous immunoglobulin-resistance in Kawasaki disease based on meta-analysis of 15 cohorts |
| title_sort | establishment and validation of risk prediction model to predict intravenous immunoglobulin resistance in kawasaki disease based on meta analysis of 15 cohorts |
| topic | Kawasaki disease Intravenous immunoglobulin Risk factor Prediction model |
| url | https://doi.org/10.1186/s13052-025-01889-w |
| work_keys_str_mv | AT shuhuiwang establishmentandvalidationofriskpredictionmodeltopredictintravenousimmunoglobulinresistanceinkawasakidiseasebasedonmetaanalysisof15cohorts AT nasun establishmentandvalidationofriskpredictionmodeltopredictintravenousimmunoglobulinresistanceinkawasakidiseasebasedonmetaanalysisof15cohorts AT panpanliu establishmentandvalidationofriskpredictionmodeltopredictintravenousimmunoglobulinresistanceinkawasakidiseasebasedonmetaanalysisof15cohorts AT weiguoqian establishmentandvalidationofriskpredictionmodeltopredictintravenousimmunoglobulinresistanceinkawasakidiseasebasedonmetaanalysisof15cohorts AT qiuqinxu establishmentandvalidationofriskpredictionmodeltopredictintravenousimmunoglobulinresistanceinkawasakidiseasebasedonmetaanalysisof15cohorts AT daopingyang establishmentandvalidationofriskpredictionmodeltopredictintravenousimmunoglobulinresistanceinkawasakidiseasebasedonmetaanalysisof15cohorts AT mingyangzhang establishmentandvalidationofriskpredictionmodeltopredictintravenousimmunoglobulinresistanceinkawasakidiseasebasedonmetaanalysisof15cohorts AT miaohou establishmentandvalidationofriskpredictionmodeltopredictintravenousimmunoglobulinresistanceinkawasakidiseasebasedonmetaanalysisof15cohorts AT yechen establishmentandvalidationofriskpredictionmodeltopredictintravenousimmunoglobulinresistanceinkawasakidiseasebasedonmetaanalysisof15cohorts AT guanghuiqian establishmentandvalidationofriskpredictionmodeltopredictintravenousimmunoglobulinresistanceinkawasakidiseasebasedonmetaanalysisof15cohorts AT chunmeigao establishmentandvalidationofriskpredictionmodeltopredictintravenousimmunoglobulinresistanceinkawasakidiseasebasedonmetaanalysisof15cohorts AT lingsun establishmentandvalidationofriskpredictionmodeltopredictintravenousimmunoglobulinresistanceinkawasakidiseasebasedonmetaanalysisof15cohorts AT haitaolv establishmentandvalidationofriskpredictionmodeltopredictintravenousimmunoglobulinresistanceinkawasakidiseasebasedonmetaanalysisof15cohorts |